Sahte Web Sitelerinin Sınıflandırma Algoritmaları İle Tespit Edilmesi

Korkmaz, Adem; Büyükgöze, Selma

doi:10.31590/ejosat.598036

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…These models were selected due to their proven efficacy in handling categorical data and their adaptability to varied dataset characteristics, as evidenced in prior research. Each model's performance was rigorously evaluated based on pertinent metrics including accuracy, precision, recall, and F1-score [42], ensuring a comprehensive assessment of the model's predictive capabilities.…”

Section: Discussionmentioning

confidence: 99%

Harnessing Machine Learning to Enhance Global Road Safety: A Comprehensive Review

Bulut

2024

Politeknik Dergisi

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As global urbanization accelerates, road safety remains a pressing concern, underscored by escalating traffic accidents and fatalities. Road Traffic Injuries (RTI) have become the eighth leading cause of death worldwide. The article delves deep into the potential of machine learning in predicting traffic accidents, their severity, and causal factors. This study comprehensively evaluates machine learning models on traffic accident records sourced from the Addis Ababa City Police Department. Comprising 12,316 records with 15 features, the dataset underwent preprocessing techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and Min-Max scaling. Five algorithms – Random Forest (RF), Gaussian Naive Bayes, CatBoostClassifier, LightGBM, and XGBoost – were tested for their prediction accuracy. The findings spotlight the dominance of the RF model, achieving a peak accuracy of 92.2% post-SMOTE and Min-Max application. A comparative analysis with existing literature showed that while RF is a recurrently effective model across various datasets, data preprocessing and model suitability to specific datasets is paramount. This study underscores the potential of machine learning in traffic accident analysis and the nuanced choices researchers must make for optimal outcomes.

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Section: Discussionmentioning

confidence: 99%

Harnessing Machine Learning to Enhance Global Road Safety: A Comprehensive Review

Bulut

2024

Politeknik Dergisi

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“…In the developed models of this study, the weight and activation parameters conduct the classification task at four different quantification levels. One of the most significant tools used in evaluating the performance of a model in AI-Based classification applications is the confusion matrix [31]. It paves a way to explore the relationships between the performance and test outputs better.…”

Section: Performance Criterionsmentioning

confidence: 99%

An Efficient Classification of Rice Variety with Quantized Neural Networks

Tasci,

Istanbullu,

Kosunalp

et al. 2023

Electronics

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Rice, as one of the significant grain products across the world, features a wide range of varieties in terms of usability and efficiency. It may be known with various varieties and regional names depending on the specific locations. To specify a particular rice type, different features are considered, such as shape and color. This study uses an available dataset in Turkey consisting of five different varieties: Ipsala, Arborio, Basmati, Jasmine, and Karacadag. The dataset introduces 75,000 grain images in total; each of the 5 varieties has 15,000 samples with a 256 × 256-pixel dimension. The main contribution of this paper is to create Quantized Neural Network (QNN) models to efficiently classify rice varieties with the purpose of reducing resource usage on edge devices. It is well-known that QNN is a successful method for alleviating high computational costs and power requirements in response to many Deep Learning (DL) algorithms. These advantages of the quantization process have the potential to provide an efficient environment for artificial intelligence applications on microcontroller-driven edge devices. For this purpose, we created eight different QNN networks using the MLP and Lenet-5-based deep learning models with varying quantization levels to be trained by the dataset. With the Lenet-5-based QNN network created at the W3A3 quantization level, a 99.87% classification accuracy level was achieved with only 23.1 Kb memory size used for the parameters. In addition to this tremendous benefit of memory usage, the number of billion transactions per second (GOPs) is 23 times less than similar classification studies.

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“…Although various research endeavors have been undertaken in AI across multiple fields, its impact on education has also been investigated. AI is applied across several disciplines, including law, science, mathematics, health, engineering, and architecture (Korkmaz & Büyükgöze, 2019;Taşçi & Çelebi, 2020). Research in this field is gaining momentum, and progress is being made through continued research and development activities.…”

Section: Introductionmentioning

confidence: 99%

The Role of Artificial Intelligence in Higher Education: ChatGPT Assessment for Anatomy Course

Talan¹,

Kalinkara²

2023

Uluslararası Yönetim Bilişim Sistemleri Ve Bilgisayar Bilimleri Dergisi

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The extensive research conducted over a prolonged period of time has resulted in the rapid advancement of artificial intelligence (AI) technology, leading to the development of numerous applications. One such recent AI application is ChatGPT, an AI chatbot that has gained millions of users in a short span of time, and can read and write texts. The aim of this study is to compare the performance of ChatGPT in an anatomy course with that of undergraduate students. The participants were students from the Faculty of Health Sciences at a state university in Turkey, who had previously taken an anatomy course. The examination was in the form of a multiple-choice test consisting of 40 items. It was found that ChatGPT outperformed the students in the examination.

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Sahte Web Sitelerinin Sınıflandırma Algoritmaları İle Tespit Edilmesi

Cited by 8 publications

References 7 publications

Harnessing Machine Learning to Enhance Global Road Safety: A Comprehensive Review

Harnessing Machine Learning to Enhance Global Road Safety: A Comprehensive Review

An Efficient Classification of Rice Variety with Quantized Neural Networks

The Role of Artificial Intelligence in Higher Education: ChatGPT Assessment for Anatomy Course

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